Parallel Magnetic Resonance Imaging (pMRI) apparatus acquire signals in parallel using an array of detectors (e.g., coils). The detectors may be arranged in a phased array of coils. Individual coils in the phased array are generally designed to have localized sensitivity, or the sensitivities of coils may be designed to be smooth over a field of view (FOV) and may overlap. Reconstruction in pMRI depends on understanding the actual sensitivity of these coils in a pMRI apparatus during an MRI session. For example, some arrays may have 2, 4, 8, 16, 32, or more coils, some of which may have slight individualities, both as manufactured and as deployed. Additional individualities may appear during an imaging session based on the unknown position of the detectors (e.g. in a flexible array) or due to actual dynamic conditions during image acquisition (e.g., motion, noise, variable coil loading). Thus, actual results may differ from those predicted theoretically. Understanding the actual sensitivity of coils may depend, at least in part, on a per-session calibration of the coils.
pMRI techniques that under sample k-space have been developed. These techniques typically acquire additional coil sensitivity information to offset the effect of the under sampling. The additional coil sensitivity information has conventionally been computed from additional k-space lines acquired specifically for calibration. These lines may be referred to as auto-calibration signal (ACS) lines. Conventionally, a small number of ACS lines are acquired before and/or during a scan to help estimate sensitivities. Thus, at least part of the benefit of under sampling is lost due to the additional time required to acquire ACS lines. Under sampling may lead to a reduction in scan time that is referred to as a reduction factor (R). In some conventional systems, (R−1) extra ACS lines are acquired from near the center of k-space at positions like mΔky, where m counts from 1 to (R−1). With the calibration data available, a reconstruction that includes calculating missing k-space data is based, at least in part, on coil sensitivities computed from the ACS lines may be undertaken.
GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) is one technique that reconstructs in k-space by calculating missing k-space data based, at least in part, on information acquired from ACS lines. In GRAPPA, the additionally acquired ACS lines SkACS are used to automatically derive a set of linear weights nk(m). In GRAPPA, missing k-space data can be calculated from measured k-space data in light of the sensitivities and weights to form a complete dense k-space, resulting in a full field of view (FOV) after Fourier transformation.
In GRAPPA, the component coil signals Sk(k) are fit to a single component coil ACS signal. This procedure is repeated for the component coils. Thus, it can be seen that GRAPPA uses multiple k-space lines from multiple coils to fit one single coil ACS line. This results in improved accuracy in the fit procedure over, for example, a system determined from external reference data, which could include static maps of coil sensitivity. Central k-space lines may be fit to calculate reconstruction parameters. Since this fitting procedure involves global information, it may be less affected by local inhomogeneities.